Kinds of Minds

In Beyond AI, J. Storrs Hall offers “a must-read for anyone interested
in the future of the human-machine civilization,” says Ray Kurzweil. In
this excerpt, Hall suggests a classification of the different stages an
AI might go through, from “hypohuman” (most existing AIs) to
“hyperhuman” (similar to “superintelligence”).

“Perhaps our questions about artificial intelligence are a bit like
inquiring after the temperament and gait of a horseless carriage.”

K. Eric Drexler

Classifications

Now we will classify the different stages AI might go through by using
the Greek prepositions. These have been adopted into English as
prefixes, particularly in scientific usage. In some cases the concepts
have been applied to advancing AI before and in other cases not. The
reason for introducing these new terms is they provide a framework that
puts any given level of expected AI capability in perspective vis-à-vis
the other levels, and in comparison to human intelligence.

Hypohuman AI

Hypo means below or under (think hypodermic, under the skin;
hypothermia
or hypoglycemia, below normal temperature or blood sugar), including, in
the original Greek, under the moral or legal subjection of. Isaac
Asimov’s robots are (mostly) hypohuman, in both senses of hypo: they are
not quite as smart as humans, and they are subject to our rule. Most
existing AI is arguably hypohuman, as well (Deep Blue to the contrary
notwithstanding). As long as it stays that way, the only thing we have
to worry about is that there will be human idiots putting their AI
idiots in charge of things they both don’t understand. All the
discussion of formalist float applies, especially the part about
feedback.

Diahuman AI

Image courtesy of
20th Century Fox.

Dia means through or across in Greek (diameter,
diagonal), and the Latin trans means the same thing, but the
commonly heard transhuman doesn’t apply here. Transhuman
refers to humans as opposed to AIs, humans who have been enhanced (by
whatever means) and are in a transitional state between human and fully
posthuman, whatever that may be. Neither concept is very useful here.

By diahuman, I mean AIs in the stage where AI capabilities are crossing
the range of human intelligence. It’s tempting to call this
human-equivalent, but the idea of equivalence is misleading. It’s
already apparent that some AI abilities (e.g., chess playing) are beyond
the human scale, while others (e.g., reading and writing) haven’t
reached it yet.

Thus diahuman refers to a phase of AI development (and only by extension
to an individual AI in that phase), and this is fuzzy because the limits
of human (and AI) capability are fuzzy. It’s hard to say which
capabilities are important in the comparison. I would claim that AI is
entering the early stages of the diahuman phase right now; there are
humans who, like today’s AIs, don’t learn well and who function
competently only at simple jobs for which they must be
trained.

The core of the diahuman phase, however, will be the development of
autogenous learning. In the latter stages, AIs, like the brightest
humans, will be completely autonomous, not only learning what they need
to know but also deciding what they need to learn.

Diahuman AIs will be valuable and will undoubtedly attract significant
attention and resources to the AI enterprise. They are likely to cause
something of a stir in philosophy and perhaps religion, as well.
However, they will not have a significant impact on the human condition.
(The one exception might be economically, in the case that diahuman AI
lingers so long that Moore’s law makes human-equivalent robots very
cheap compared to human labor. But I’m assuming that we will probably
have advanced past the diahuman stage by then.)

Parahuman AI

Image courtesy of MIT.

Para means alongside (paralegal, paramedic). The
concept of designing a system that a human is going to be part of dates
back to cybernetics (although all technology throughout history had to
be designed so that humans could operate it, in some sense).

Parahuman AI will be built around more and more sophisticated theories
of how humans work. The PC of the future ought to be a parahuman AI. MIT
roboticist Cynthia Brazeal’s sociable robots are the likely forerunners
of a wide variety of robots that will interact with humans in many kinds
of situations.

The upside of parahuman AI is that it will enhance the interface between
our native senses and abilities, adapted as they are for a hunting and
gathering bipedal ape, and the increasingly formalized and mechanized
world we are building. The parahuman AI should act like a lawyer, a
doctor, an accountant, and a secretary, all with deep knowledge and
endless patience. Once AI and cognitive science have acquired a solid
understanding of how we learn, parahuman AI teachers could be built
which would model in detail how each individual student was absorbing
the material, ultimately finding the optimal presentation for
understanding and motivation.

The downside is simply the same effect, put to work with slimier
motives: the parahuman advertising AI, working for corporations or
politicians, could know just how to tweak your emotions and gain your
trust without actually being trustworthy. It would be the equivalent of
an individualized artificial con man. Note by the way that of the two
human elements that were part of the original cybernetic anti-aircraft
control theory, one of them, the pilot of the plane being shot at,
didn’t want to be part of the system but was, willy-nilly.

Parahuman is a characterization that does not specify a level of
intellectual capability compared to humans; it can be properly applied
to AIs at any level. Humans are fairly strongly parahuman intelligences
as well; many of our innate skills involve interacting with other
humans. Parahuman can be largely contrasted with the following term,
allohuman.

Allohuman AI

Allo means other or different (allomorph, allonym, allotrope). Although
I have argued that human intelligence is universal, there remains a vast
portion of our minds that is distinctively human. This includes the
genetically programmed representation modules, the form of our
motivations, and the sensory modalities, of which several are fairly
specific to running a human body.

It will certainly be possible to create intelligences that while being
universal nevertheless have different lower-level hardwired modalities
for sense and representation, and different higher-level motivational
structure. One simple possibility is that universal mechanism may stand
in for a much greater portion of the cognitive mechanism so that, for
example, the AI would use learned physics instead of instinctive
concepts and learned psychology instead of our folk models.

Such differences could reasonably make the AI better at certain tasks;
consider the ability to do voluminous calculations in you head. However,
if you have ever watched an experienced accountant manipulate a
calculator, you can see that the numbers almost flow through his
fingers. Built-in modalities may provide some increment of effectiveness
compared to learned ones, but not as much as you might think. Consider
reading  it’s a learned activity, and unlike talking, we don’t
just “pick it up”. But with practice, we read much faster than we can
talk or understand spoken language.

Motivations and the style and the volume of communication could also
differ markedly from the human model. The allohuman AI might resemble
Mr. Spock, or it might resemble an intelligent ant. This likely will
form the bulk of the difference between allohuman AIs and humans rather
than the varying modalities.

Like parahuman, allohuman does not imply a given level of intellectual
competence. In the fullness of time, however, the parahuman/allohuman
distinction will make less and less difference. More advanced AIs,
whether they need to interact with humans or to do something weirdly
different, will simply obtain or deduce whatever knowledge is necessary
and synthesize the skills on the fly.

Epihuman AI

Epi means upon or after (epidermis, epigram,
epitaph, epilogue). I’m using it here in a combination of
senses to mean AI that is just above the range of individual human
capabilities but that still forms a continuous range with them, and also
in the sense of what comes just after diahuman AI. That gives us what
can be a useful distinction versus further-out possibilities. (See
hyper below.)

Science fiction writer Charles Stross introduced the phrase “weakly
godlike AI”. Weakly presumably refers to the fact that such AIs would
still be bound by the laws of physics  they couldn’t perform
miracles, for example. As a writer, I’m filled with admiration for the
phrase, since weakly and godlike have such contrasting meanings that it
forces you to think when you read it for the first time, and the term
weakly is often used in a similar way, with various technical meanings,
in scientific discourse, giving a vague sense of rigor (!) to the
phrase.

The word posthuman is often used to describe what humans may be like
after various technological enhancements. Like transhuman, posthuman is
generally used for modified humans instead of synthetic AIs.

My model for what an epihuman AI would be like is to take the ten
smartest people you know, remove their egos, and duplicate them a
hundred times, so that you have a thousand really bright people willing
to apply themselves all to the same project. Alternatively, simply
imagine a very bright person given a thousand times as long to do any
given task. We can straightforwardly predict, from Moore’s law, that ten
years after the advent of a learning but not radically self-improving
human-level AI, the same software running on machinery of the same cost
would do the same human-level tasks a thousand times as fast as we. It
could, for example:

read an average book in one second with full comprehension;

take a college course and do all the homework and research in ten
minutes;

write a book, again with ample research, in two or three hours;

produce the equivalent of a human’s lifetime intellectual output,
complete with all the learning, growth, and experience involved, in a
couple of weeks.

A thousand really bright people are enough to do some substantial and
useful work. An epihuman AI could probably command an income of $100
million or more in today’s economy by means of consulting and
entrepreneurship, and it would have a net present value in excess of a
$1 billion. Even so, it couldn’t take over the world or even an
established industry. It could probably innovate well enough to become a
standout in a nascent field, though, as in Google’s case.

A thousand top people is a reasonable estimate for what the current
field of AI research is applying to the core questions and
techniques  basic, in contrast to applied, research. Thus an
epihuman AI could probably improve itself about as fast as current AI is
improving. Of course, if it did that, it wouldn’t be able to spend its
time making all that money; the opportunity cost is pretty high. It
would need to make exactly the same kind of decision that any business
faces with respect to capital reinvestment.

Whichever it may choose to do, the epihuman level characterizes an AI
that is able to stand in for a given fairly sizeable company or for a
field of academic inquiry. As more and more epihuman AIs appear, they
will enhance economic and scientific growth so that by the later stages
of the phase the total stock of wealth and knowledge will be
significantly higher than it would have been without the AIs. AIs will
be a significant sector, but no single AI would be able to rock the boat
to a great degree.

Hyperhuman AI

Hyper means over or above. In common use as an English prefix,
hyper tends to denote a greater excess than super, which means
the same thing but comes from Latin instead of Greek. (Contrast, e.g.,
supersonic, more than Mach 1, and hypersonic, more than Mach
5.)

In the original Singularity paper,
The Coming Technological
Singularity, Vernor Vinge used the phrase superhuman
intelligence. Nick Bostrom has used the term
superintelligence. Like some of the terms above, however,
superhuman has a wide range of meanings (think about Kryptonite),
and most of them are not applicable to the subject at hand. We will stay
with our Greek prefixes and finish the list with hyperhuman.

Imagine an AI that is a thousand epihuman AIs, all tightly integrated
together. Such an intellect would be capable of substantially
outstripping the human scientific community at any given task and of
comprehending the entirety of scientific knowledge as a unified whole. A
hyperhuman AI would soon begin to improve itself significantly faster
than humans could. It could spot the gaps in science and engineering
where there was low-hanging fruit and instigate rapid increases in
technological capability across the board.

It is as yet poorly understood even in the scientific community just how
much headroom remains for improvement with respect to the capabilities
of current physical technology. A mature nanotechnology, for example,
could replace the entire capital stock  all the factories,
buildings, roads, cars, trucks, airplanes, and other machines  of
the United States in a week. And that’s just using currently understood
science, with a dollop of engineering development thrown in.

Any sufficiently advanced technology, Arthur Clarke wrote, is
indistinguishable from magic. Although, I believe, any specific thing
the hyperhuman AIs might do could be understood by humans, the total
volume of work and the rate of advance would become harder and harder to
follow. Please note that any individual human is already in a similar
relationship with the whole scientific community; our understanding of
what is going on is getting more and more abstract. The average person
understands cell phones at a level of knowing that batteries have
limited lives and coverage has gaps, but not at the level of
field-effect transistor gain figures and conductive trace
electromigration phenomena.

Ten years ago the average
scientist, much
less the average user, could not have predicted that most cell phones
would contain cameras and color screens today. But we can follow, if not
predict, by understanding things at a very high level of abstraction, as
if they were magic.

Any individual hyperhuman AI would be productive, intellectually or
industrially, on the scale of the human race as a whole. As the number
of hyperhuman AIs increased, our efforts would shrink to more and more
modest proportions of the total.

Where does an eight-hundred-pound gorilla sit? According to the old
joke, anywhere he wants to. Much the same thing will be true of a
hyperhuman AI, except in instances where it has to interact with other
AIs. The really interesting question then will be, what will it want?